Multi-spectral (MS) imaging systems encounter a trade-off between spatial and spectral resolution in the hardware implementation. While high spatial resolution of MS images is desirable for many applications, the resolution of the MS images is typically degraded due to the limitations on the size, weight, and power of the sensors, e.g., sensors mounted on the board of an airplane or spacecraft.
In order to achieve super-resolved images, some conventional image processing techniques aim to improve image resolution and to mitigate this hardware limitation by using multiple successive frames from the same scene that are combined to improve spatial resolution. Some conventional methods use varies techniques to address some of hardware problems. For example, a method described in U.S. Pat. No. 7,015,954, uses multiple cameras to obtain multiple continuous images of the scene, and combines the images via warping and fading techniques, to produce a single seamless image of the scene.
Image super-resolution (SR) is generally an ill-posed inverse problem. In the context of Multi-spectral (MS) imaging, the goal is to reconstruct the high-resolution (HR) multi-channel images from their low resolution (LR) measurements. Pan-sharpening methods have been proposed to fuse the LR MS images with a HR panchromatic (PAN) image as reference. These Pan-sharpening methods can somewhat improve the spatial resolution, only to some extent, by enhancing the high-frequency components in the spatial domain.
The present disclosure addresses the technological needs of today's image processing industries and other related technology industries, by increasing resolution of multi-spectral images using a coupled analysis and synthesis dictionary (CASD) model, along with other aspects.